{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 100 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "ename": "RuntimeError",
     "evalue": "CUDA error: out of memory",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-2-ae031c0b81cd>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m    181\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    182\u001b[0m \u001b[0;31m# 开始训练\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 183\u001b[0;31m \u001b[0mD_net\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdiscriminator\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcuda\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    184\u001b[0m \u001b[0mG_net\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgenerator\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcuda\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    185\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36mcuda\u001b[0;34m(self, device)\u001b[0m\n\u001b[1;32m    303\u001b[0m             \u001b[0mModule\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    304\u001b[0m         \"\"\"\n\u001b[0;32m--> 305\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_apply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcuda\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    306\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    307\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mcpu\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_apply\u001b[0;34m(self, fn)\u001b[0m\n\u001b[1;32m    200\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_apply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    201\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mmodule\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchildren\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 202\u001b[0;31m             \u001b[0mmodule\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_apply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    203\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    204\u001b[0m         \u001b[0;32mdef\u001b[0m \u001b[0mcompute_should_use_set_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtensor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtensor_applied\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_apply\u001b[0;34m(self, fn)\u001b[0m\n\u001b[1;32m    200\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_apply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    201\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mmodule\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchildren\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 202\u001b[0;31m             \u001b[0mmodule\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_apply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    203\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    204\u001b[0m         \u001b[0;32mdef\u001b[0m \u001b[0mcompute_should_use_set_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtensor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtensor_applied\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_apply\u001b[0;34m(self, fn)\u001b[0m\n\u001b[1;32m    222\u001b[0m                 \u001b[0;31m# `with torch.no_grad():`\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    223\u001b[0m                 \u001b[0;32mwith\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mno_grad\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 224\u001b[0;31m                     \u001b[0mparam_applied\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparam\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    225\u001b[0m                 \u001b[0mshould_use_set_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcompute_should_use_set_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparam\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparam_applied\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    226\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mshould_use_set_data\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(t)\u001b[0m\n\u001b[1;32m    303\u001b[0m             \u001b[0mModule\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    304\u001b[0m         \"\"\"\n\u001b[0;32m--> 305\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_apply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcuda\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    306\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    307\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mcpu\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mRuntimeError\u001b[0m: CUDA error: out of memory"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "from torchvision import transforms, datasets\n",
    "from torch import nn, optim\n",
    "from torch.utils.data import DataLoader, sampler\n",
    "from torch.autograd import Variable\n",
    "import matplotlib.gridspec as gridspec\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "plt.rcParams['figure.figsize'] = (10.0, 8.0)\n",
    "plt.rcParams['image.interpolation'] = 'nearest'\n",
    "plt.rcParams['image.cmap'] = 'gray'\n",
    "# 超参数设置\n",
    "batch_size = 100\n",
    "NOISE_DIM = 96\n",
    "NUM_TRAIN = 500\n",
    "NUM_VAL = 50\n",
    "def deprocess_img(x):\n",
    "    return (x+1.0)/2.0\n",
    "\n",
    "# 定义一个采样函数\n",
    "class ChunkSampler(sampler.Sampler):\n",
    "    \"\"\"Samples elements sequentially from some offset.\n",
    "    Arguments:\n",
    "        num_samples: # of desired datapoints\n",
    "        start: offset where we should start selecting from\n",
    "    \"\"\"\n",
    "    def __init__(self, num_samples, start=0):\n",
    "        self.num_samples = num_samples\n",
    "        self.start = start\n",
    "\n",
    "    def __iter__(self):\n",
    "        return iter(range(self.start, self.start + self.num_samples))\n",
    "\n",
    "    def __len__(self):\n",
    "        return self.num_samples\n",
    "\n",
    "def show_images(images):\n",
    "    images = np.reshape(images, [images.shape[0], -1])\n",
    "    sqrtn = int(np.ceil(np.sqrt(images.shape[0])))\n",
    "    sqrtimg = int(np.ceil(np.sqrt(images.shape[1])))\n",
    "\n",
    "    fig = plt.figure(figsize=(sqrtn, sqrtn))\n",
    "    gs = gridspec.GridSpec(sqrtn, sqrtn)\n",
    "    gs.update(wspace=0.05, hspace=0.05)\n",
    "\n",
    "    for i, img in enumerate(images):\n",
    "        ax = plt.subplot(gs[i])\n",
    "        plt.axis('off')\n",
    "        ax.set_xticklabels([])\n",
    "        ax.set_yticklabels([])\n",
    "        ax.set_aspect('equal')\n",
    "        plt.imshow(img.reshape([sqrtimg, sqrtimg]))\n",
    "    plt.show()\n",
    "    return\n",
    "data_tf = transforms.Compose([transforms.ToTensor(), transforms.Normalize([0.5], [0.5])])\n",
    "train_set = datasets.MNIST('./', train=True, download=True, transform=data_tf)\n",
    "# 取MNIST训练集的前50000张作为train_data\n",
    "train_data = DataLoader(train_set, batch_size=batch_size, sampler=ChunkSampler(NUM_TRAIN, 0))\n",
    "val_set = datasets.MNIST('./', train=True, download=True, transform=data_tf)\n",
    "# 取MNIST训练集索引为50000-55000的5000张图片作为val_data\n",
    "val_data = DataLoader(val_set, batch_size=batch_size, sampler=ChunkSampler(NUM_VAL, NUM_TRAIN))\n",
    "\n",
    "imgs = deprocess_img(train_data.__iter__().next()[0].view(batch_size, 784)).numpy().squeeze()\n",
    "show_images(imgs)\n",
    "\n",
    "# 定义判别器\n",
    "class discriminator(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(discriminator, self).__init__()\n",
    "        self.conv1 = nn.Sequential(                     # b, 1, 28, 28\n",
    "            nn.Conv2d(1, 32, kernel_size=5, padding=2),   # b, 32, 28, 28\n",
    "            nn.LeakyReLU(0.2, True),\n",
    "            nn.AvgPool2d(2, 2)          # b, 32, 14, 14\n",
    "        )\n",
    "        self.conv2 = nn.Sequential(\n",
    "            nn.Conv2d(32, 64, kernel_size=5, padding=2),  # b, 64, 14, 14\n",
    "            nn.LeakyReLU(0.2, True),\n",
    "            nn.AvgPool2d(2, 2)    # b, 64, 7, 7\n",
    "        )\n",
    "        self.fc = nn.Sequential(\n",
    "            nn.Linear(64*7*7, 32),\n",
    "            nn.LeakyReLU(0.2, True),\n",
    "            nn.Linear(32, 1),\n",
    "            nn.Sigmoid()   # 将输出映射到(0, 1)\n",
    "        )\n",
    "    def forward(self, x):\n",
    "        x = self.conv1(x)\n",
    "        x = self.conv2(x)\n",
    "        x = x.view(x.size(0), -1)\n",
    "        x = self.fc(x)\n",
    "        return x\n",
    "\n",
    "class generator(nn.Module):\n",
    "    def __init__(self, noise_dim=NOISE_DIM):\n",
    "        super(generator, self).__init__()\n",
    "        self.fc = nn.Sequential(\n",
    "            nn.Linear(noise_dim, 256),\n",
    "            nn.ReLU(True),\n",
    "            nn.BatchNorm1d(32),\n",
    "            nn.Linear(32, 3*3*128),\n",
    "            nn.ReLU(True),\n",
    "            nn.BatchNorm1d(3 * 3 * 128),\n",
    "        )\n",
    "        self.conv = nn.Sequential(\n",
    "            nn.ConvTranspose2d(128, 64, kernel_size=4, stride=2, padding=1),   # b, 64, 14, 14\n",
    "            nn.ReLU(True),\n",
    "            nn.BatchNorm2d(64),\n",
    "            nn.ConvTranspose2d(64, 1, 4, 2, padding=1),    # b, 1, 28, 28\n",
    "            nn.Tanh()\n",
    "        )\n",
    "    def forward(self, x):\n",
    "        x = self.fc(x)\n",
    "        x = x.view(x.size(0), 128, 7, 7)\n",
    "        x = self.conv(x)\n",
    "        return x\n",
    "\n",
    "bce_loss = nn.BCEWithLogitsLoss()\n",
    "\n",
    "# 定义判别器的损失函数\n",
    "def discriminator_loss(logits_real, logits_fake):\n",
    "    size = logits_real.size(0)\n",
    "    true_labels = Variable(torch.ones(size, 1).float().cuda())\n",
    "    false_labels = Variable(torch.zeros(size, 1).float().cuda())\n",
    "    loss = bce_loss(logits_real, true_labels) + bce_loss(logits_fake, false_labels)\n",
    "    return loss\n",
    "\n",
    "# 定义生成器的损失函数\n",
    "def generator_loss(logits_fake):\n",
    "    size = logits_fake.size(0)\n",
    "    true_labels = Variable(torch.ones(size, 1).float().cuda())\n",
    "    loss = bce_loss(logits_fake, true_labels)\n",
    "    return loss\n",
    "\n",
    "# 定义优化器\n",
    "def get_optimizer(net):\n",
    "    optimizer = optim.Adam(net.parameters(), lr=3e-4, betas=(0.5, 0.999))\n",
    "    return optimizer\n",
    "\n",
    "# 定义训练函数\n",
    "def train_gan(D_net, G_net, D_optimizer, G_optimizer,\n",
    "              show_every=250, noise_size=96, num_epochs=10):\n",
    "    iter_count = 0\n",
    "    for epoch in range(num_epochs):\n",
    "        for x, _ in train_data:\n",
    "            bs = x.size(0)\n",
    "            # 判别网络\n",
    "            real_data = Variable(x).cuda()\n",
    "            logits_real = D_net(real_data)\n",
    "\n",
    "            # 使用先验分布torch.rand()产生区间(0, 1)间的数据\n",
    "            sample_noise = (torch.rand(bs, noise_size) - 0.5)/0.5   # ([-1, 1])\n",
    "            g_fake_seed = Variable(sample_noise).cuda()\n",
    "            # 生成假的数据\n",
    "            fake_images = G_net(g_fake_seed)\n",
    "            # 生成数据送入判别器的得分\n",
    "            logits_fake = D_net(fake_images)\n",
    "            # 判别器的误差函数\n",
    "            discriminator_error = discriminator_loss(logits_real, logits_fake)\n",
    "            D_optimizer.zero_grad()\n",
    "            discriminator_error.backward()\n",
    "            D_optimizer.step()\n",
    "\n",
    "            # 使用生成网络产生新的数据\n",
    "            sample_noise = (torch.rand(bs, noise_size) - 0.5)/0.5\n",
    "            g_fake_seed = Variable(sample_noise).cuda()\n",
    "            fake_images = G_net(g_fake_seed)\n",
    "            logits_fake = D_net(fake_images)\n",
    "            generator_error = generator_loss(logits_fake)\n",
    "            G_optimizer.zero_grad()\n",
    "            generator_error.backward()\n",
    "            G_optimizer.step()\n",
    "\n",
    "            if (iter_count % show_every == 0):\n",
    "                print(\"epoch: {}, Iter: {}, D:{:.4}, G:{:.4}\".format(epoch, iter_count, discriminator_error.data, generator_error.data))\n",
    "                imgs_numpy = deprocess_img(fake_images.data.cpu().numpy())\n",
    "                show_images(imgs_numpy)\n",
    "                print(\"iter_count: {}\".format(iter_count))\n",
    "            iter_count += 1\n",
    "\n",
    "# 开始训练\n",
    "D_net = discriminator().cuda()\n",
    "G_net = generator().cuda()\n",
    "\n",
    "D_optimizer = get_optimizer(D_net)\n",
    "G_optimizer = get_optimizer(G_net)\n",
    "\n",
    "train_gan(D_net, G_net, D_optimizer, G_optimizer, num_epochs=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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